-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urp
> elainen Zhang)/JOP/UZ_JOP2021_Replication/Analysis/logSTATA/003_balanceTest.s
> mcl
  log type:  smcl
 opened on:   6 Nov 2021, 19:49:41

. 
. 
. *****************************************************************************
> **
. /*                                   RENEWABLES_VOTING (URPELAINEN & ZHANG)  
>                             */
. *****************************************************************************
> **
. 
. /* 
> 
> File Name:      003_balanceTest.do
> 
> By:                             Alice Tianbo Zhang (alice.tianbo.zhang@gmail.
> com)
> 
> Last Edited:    10/11/2021
> 
> Purpose:                1. Summary statistics of votes wind panel and electio
> n district panel
>                                 2. Exploratory data analysis of main variable
> s
>                                 3. Conduct balance test using ACS panel
> 
> Data Used:      votes_wind_panel.dta
>                                 election_district_panel.dta     
>                                 ACS_panel_balanceTest_recodeVar.dta
>                                 
> */
. 
. ** Install packages
. *ssc install reghdfe
. *ssc install tabout
. *ssc install latab
. 
. *****************************************************************************
> **
. /*                                               TABLE A1                    
>                                                 */
. *****************************************************************************
> **
. cd "$rootDir/$dataDir/Final"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Data/Final

. use votes_wind_panel.dta, clear

. 
. eststo clear

. eststo: estpost tabstat cum_count_turbine cum_capacity_turbine count_turbine 
> capacity_turbine ///
>                                 mean_wp std_wp min_wp median_wp max_wp ///
>                                 pro_env anti_env ///
>                                 incumbvotesmajorpercent demvotesmajorpercent 
> repvotesmajorpercent, ///
>                                 stat(mean sd min max count) col(stat)

Summary statistics: mean sd min max count
     for variables: cum_count_turbine cum_capacity_turbine count_turbine capaci
> ty_turbine mean_wp std_wp min_wp median_wp max_wp pro_env anti_env incumbvote
> smajorpercent demvotesmajorpercent repvotesmajorpercent

             |   e(mean)      e(sd)     e(min)     e(max)   e(count) 
-------------+-------------------------------------------------------
cum_count_~e |  71.39582   408.1722          0       5040       2870 
cum_capaci~e |   45.5735   209.7106          0    2935.97       2870 
count_turb~e |  6.333798   35.61911          0        680       2870 
capacity_t~e |  11.18447   65.44925          0    1320.95       2864 
     mean_wp |  1.695802   .5240618          1     4.0793       2870 
      std_wp |  .5198966   .3189492          0   1.527079       2870 
      min_wp |   1.12892   .3351696          1          2       2870 
   median_wp |  1.571429    .591227          1          4       2870 
      max_wp |  3.560976   1.750051          1          7       2870 
     pro_env |  56.38361   40.54201          0        100       2868 
    anti_env |  40.15067   40.30665          0        100       2868 
incumbvote~t |  69.82382   13.83102      38.98        100       2602 
demvotesma~t |  55.38949   23.04784          0        100       2868 
repvotesma~t |  44.28038   22.95421          0        100       2867 
(est1 stored)

. local summary_var cum_count_turbine cum_capacity_turbine count_turbine capaci
> ty_turbine mean_wp std_wp min_wp median_wp max_wp pro_env anti_env incumbvote
> smajorpercent demvotesmajorpercent repvotesmajorpercent

. 
. ** Indent balance variables in table
. foreach v of varlist `summary_var' {
  2.         label variable `v' `"\hspace{0.2cm} `: variable label `v''"'
  3.         }       

. 
. ** Output table to LaTeX
. cd "$rootDir/$resultDir/Tables"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Results/Tables

. esttab, cells("mean(fmt(%8.2f)) sd(fmt(%8.2f)) min(fmt(%8.0f)) max(fmt(%8.0f)
> ) count(fmt(%8.0f))") coll(Mean SD Min Max Obs) ///
>                 varwidth(100) label wrap nostar unstack noobs nonote nomtitle
> s nonumbers

-------------------------------------------------------------------------------
-----------------------------------------------------------------------------
---------
                                                                               
>                               Mean           SD          Min          Max    
>       Obs
-------------------------------------------------------------------------------
-----------------------------------------------------------------------------
---------
\hspace{0.2cm} Cumulative number of wind turbines                              
>                              71.40       408.17            0         5040    
>      2870
\hspace{0.2cm} Cumulative capacity of wind turbines (MW)                       
>                              45.57       209.71            0         2936    
>      2870
\hspace{0.2cm} Number of wind turbines                                         
>                               6.33        35.62            0          680    
>      2870
\hspace{0.2cm} Capacity of wind turbines (MW)                                  
>                              11.18        65.45            0         1321    
>      2864
\hspace{0.2cm} mean of zonal wind potential                                    
>                               1.70         0.52            1            4    
>      2870
\hspace{0.2cm} std of zonal wind potential                                     
>                               0.52         0.32            0            2    
>      2870
\hspace{0.2cm} min of zonal wind potential                                     
>                               1.13         0.34            1            2    
>      2870
\hspace{0.2cm} median of zonal wind potential                                  
>                               1.57         0.59            1            4    
>      2870
\hspace{0.2cm} max of zonal wind potential                                     
>                               3.56         1.75            1            7    
>      2870
\hspace{0.2cm} Pro-environment vote share                                      
>                              56.38        40.54            0          100    
>      2868
\hspace{0.2cm} Anti-environment vote share                                     
>                              40.15        40.31            0          100    
>      2868
\hspace{0.2cm} Incumbent vote share                                            
>                              69.82        13.83           39          100    
>      2602
\hspace{0.2cm} Democratic candidate vote share                                 
>                              55.39        23.05            0          100    
>      2868
\hspace{0.2cm} Republican candidate vote share                                 
>                              44.28        22.95            0          100    
>      2867
-------------------------------------------------------------------------------
-----------------------------------------------------------------------------
---------

. 
. esttab using TableA1.tex, booktabs replace ///
>                 refcat(cum_count_turbine "\emph{Wind Turbine Installation}" m
> ean_wp "\emph{Zonal Wind Potential}" pro_env "\emph{Roll Call Vote Outcome }"
>  incumbvotesmajorpercent "\emph{Election Outcome}" , nolabel) ///
>                 cells("mean(fmt(%8.2f)) sd(fmt(%8.2f)) min(fmt(%8.0f)) max(fm
> t(%8.0f)) count(fmt(%8.0f))") coll(Mean SD Min Max Obs) ///
>                 varwidth(100) label gaps nostar noobs nonote nomtitles nonumb
> ers width(\hsize)
(output written to TableA1.tex)

. 
.                 
. *****************************************************************************
> **
. /*                                               TABLE A2                    
>                                                 */
. *****************************************************************************
> **
. cd "$rootDir/$dataDir/Final"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Data/Final

. use election_district_panel.dta, clear

. 
. ** Label variables and create turnout variable
. gen turnout = (votes1 + votes2 + votes3 + votes4)/1000

. label variable incumbvotesmajorpercent "Incumbent candidates vote share (\%)"

. label variable demvotesmajorpercent "Democratic candidates vote share (\%)"

. label variable repvotesmajorpercent "Republican candidates vote share (\%)"

. label variable thirdvotestotalpercent "Third party candidates vote share (\%)
> " 

. label variable turnout "Total number of votes (thousand)"

. label variable demhouse "Number of Democrats in the House"

. label variable rephouse "Number of Republicans in the House"

. label variable indhouse "Number of Independents in the House"

. 
. tabstat turnout demvotesmajorpercent repvotesmajorpercent thirdvotestotalperc
> ent ///
>                 incumbvotesmajorpercent demhouse rephouse indhouse, by(year) 
> statistics(mean sd min max) nototal long

year        stats |   turnout  demvot~t  repvot~t  thirdv~t  incumb~t  demhouse
>   rephouse  indhouse
------------------+------------------------------------------------------------
--------------------
2004         mean |  263.2506  54.07366  45.92634  2.008571  71.50067  201.0139
>        233         1
               sd |  57.74688   23.9887   23.9887  3.508557  12.66734  .2361125
>          0         0
              min |   108.783         0         0         0      48.3       201
>        233         1
              max |   407.291       100       100     22.23       100       205
>        233         1
------------------+------------------------------------------------------------
--------------------
2006         mean |  191.2619  59.63516  40.36484         0  69.77866       232
>   203.3136   .010453
               sd |   52.8825  22.07324  22.07324         0  14.93076         0
>   3.056449  .1018816
              min |    58.883         0         0         0     38.98       232
>        203         0
              max |    315.18       100       100         0       100       232
>        233         1
------------------+------------------------------------------------------------
--------------------
2008         mean |  279.8084  61.02558  38.97442         0  70.71954       257
>   178.5263         0
               sd |    60.159  21.69901  21.69901         0  14.24133         0
>   3.595308         0
              min |   110.955         0         0         0      43.8       257
>        178         0
              max |   419.698       100       100         0       100       257
>        203         0
------------------+------------------------------------------------------------
--------------------
2010         mean |  202.3119  51.53596  48.46403         0  64.72292  194.1228
>        242         0
               sd |  50.47153  19.23291  19.23291         0  12.95866  8.417098
>          0         0
              min |         0         0         0         0     40.32       193
>        242         0
              max |   331.258       100       100         0       100       257
>        242         0
-------------------------------------------------------------------------------
--------------------

. 
. eststo clear 

. foreach y in 2004 2006 2008 2010{
  2. 
.         estpost summarize turnout demvotesmajorpercent repvotesmajorpercent t
> hirdvotestotalpercent ///
>                                                 incumbvotesmajorpercent demho
> use rephouse indhouse if year == `y'
  3.                 
.         est sto CY`y'
  4. }

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
     turnout |       287        287   263.2506   3334.703   57.74688    108.783
>     407.291   75552.92 
demvotesma~t |       287        287   54.07366   575.4576    23.9887          0
>         100   15519.14 
repvotesma~t |       287        287   45.92634   575.4576    23.9887          0
>         100   13180.86 
thirdvotes~t |       287        287   2.008571   12.30997   3.508557          0
>       22.23     576.46 
incumbvote~t |       268        268   71.50067   160.4616   12.66734       48.3
>         100   19162.18 
    demhouse |       287        287   201.0139   .0557491   .2361125        201
>         205      57691 
    rephouse |       287        287        233          0          0        233
>         233      66871 
    indhouse |       287        287          1          0          0          1
>           1        287 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
     turnout |       287        287   191.2619   2796.558    52.8825     58.883
>      315.18   54892.17 
demvotesma~t |       287        287   59.63516   487.2281   22.07324          0
>         100   17115.29 
repvotesma~t |       287        287   40.36484   487.2281   22.07324          0
>         100   11584.71 
thirdvotes~t |       287        287          0          0          0          0
>           0          0 
incumbvote~t |       261        261   69.77866   222.9275   14.93076      38.98
>         100   18212.23 
    demhouse |       287        287        232          0          0        232
>         232      66584 
    rephouse |       287        287   203.3136   9.341878   3.056449        203
>         233      58351 
    indhouse |       287        287    .010453   .0103799   .1018816          0
>           1          3 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
     turnout |       285        285   279.8084   3619.106     60.159    110.955
>     419.698   79745.39 
demvotesma~t |       285        285   61.02558   470.8469   21.69901          0
>         100   17392.29 
repvotesma~t |       285        285   38.97442   470.8469   21.69901          0
>         100   11107.71 
thirdvotes~t |       285        285          0          0          0          0
>           0          0 
incumbvote~t |       260        260   70.71954   202.8155   14.24133       43.8
>         100   18387.08 
    demhouse |       285        285        257          0          0        257
>         257      73245 
    rephouse |       285        285   178.5263   12.92624   3.595308        178
>         203      50880 
    indhouse |       285        285          0          0          0          0
>           0          0 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
     turnout |       285        285   202.3119   2547.375   50.47153          0
>     331.258   57658.89 
demvotesma~t |       285        285   51.53596   369.9048   19.23291          0
>         100   14687.75 
repvotesma~t |       285        285   48.46403   369.9048   19.23291          0
>         100   13812.25 
thirdvotes~t |       285        285          0          0          0          0
>           0          0 
incumbvote~t |       257        257   64.72292   167.9269   12.95866      40.32
>         100   16633.79 
    demhouse |       285        285   194.1228   70.84754   8.417098        193
>         257      55325 
    rephouse |       285        285        242          0          0        242
>         242      68970 
    indhouse |       285        285          0          0          0          0
>           0          0 

. 
. esttab CY2004 CY2006 CY2008 CY2010, main(mean %8.2f) aux(sd %8.2f) varwidth(3
> 0) ///
>                                                                              
>       nostar nonote nonumber label mtitles("2004" "2006" "2008" "2010") width
> (\hsize)              

-------------------------------------------------------------------------------
---
                                       2004         2006         2008         2
> 010
-------------------------------------------------------------------------------
---
Total number of votes (thous~)       263.25       191.26       279.81       202
> .31
                                    (57.75)      (52.88)      (60.16)      (50.
> 47)

Democratic candidates vote s~e        54.07        59.64        61.03        51
> .54
                                    (23.99)      (22.07)      (21.70)      (19.
> 23)

Republican candidates vote s~e        45.93        40.36        38.97        48
> .46
                                    (23.99)      (22.07)      (21.70)      (19.
> 23)

Third party candidates vote ~r         2.01         0.00         0.00         0
> .00
                                     (3.51)       (0.00)       (0.00)       (0.
> 00)

Incumbent candidates vote sh~         71.50        69.78        70.72        64
> .72
                                    (12.67)      (14.93)      (14.24)      (12.
> 96)

Number of Democrats in the H~e       201.01       232.00       257.00       194
> .12
                                     (0.24)       (0.00)       (0.00)       (8.
> 42)

Number of Republicans in the~u       233.00       203.31       178.53       242
> .00
                                     (0.00)       (3.06)       (3.60)       (0.
> 00)

Number of Independents in th~o         1.00         0.01         0.00         0
> .00
                                     (0.00)       (0.10)       (0.00)       (0.
> 00)
-------------------------------------------------------------------------------
---
Observations                            287          287          285          
> 285
-------------------------------------------------------------------------------
---

.                                                                    
. ** Output table to LaTeX
. cd "$rootDir/$resultDir/Tables"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Results/Tables

. esttab CY2004 CY2006 CY2008 CY2010 using TableA2.tex, booktabs replace ///
>                                                                              
>      main(mean %8.2f) aux(sd %8.2f) stats(N, labels("Districts") fmt(0)) ///
>                                                                              
>      varwidth(30) gaps nostar nonote nonumber label ///
>                                                                              
>      mtitles("2004" "2006" "2008" "2010") width(\hsize)            
(output written to TableA2.tex)

.                 
.                 
. *****************************************************************************
> **
. /*                                                FIGURE 2                   
>                                                 */
. *****************************************************************************
> **
. cd "$rootDir/$dataDir/Final"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Data/Final

. use votes_wind_panel.dta, clear

. 
. *----------------------- Growth of wind power by year  ----------------------
> *
. capture drop mean_count mean_capacity

. bysort year: egen mean_count = mean(cum_count_turbine)

. bysort year: egen mean_capacity = mean(cum_capacity_turbine)

. 
. graph twoway scatter mean_capa year, scheme(vg_s1c) graphregion(color(white))
>  ytitle("Mean Wind Capacity by District (MW)") yscale(range(0 120)) ylabel(0(
> 20)120)

. 
. cd "$rootDir/$graphDir"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Results/Figures

. graph export fg2a.pdf, replace
(file /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen
>  Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures/fg2a.pdf written in PDF fo
> rmat)

. 
. graph twoway scatter mean_count year, scheme(vg_s1c) graphregion(color(white)
> ) ytitle("Mean Wind Count by District") yscale(range(0 120)) ylabel(0(20)120)

. cd "$rootDir/$graphDir"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Results/Figures

. graph export fg2b.pdf, replace
(file /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen
>  Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures/fg2b.pdf written in PDF fo
> rmat)

. 
. *****************************************************************************
> **
. /*                                              TABLE 1                      
>                                         */
. *****************************************************************************
> **
. cd "$rootDir/$dataDir/Final"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Data/Final

. use votes_wind_panel.dta, clear

. 
. ** Create dummy for treatment
. egen dtreat = max(cum_count_turbine > 0 | cum_capacity_turbine > 0), by(state
>  district)

. 
. ** Summary of observations
. unique year 
Number of unique values of year is  10
Number of records is  2870

. unique state
Number of unique values of state is  25
Number of records is  2870

. unique panelID
Number of unique values of panelID is  287
Number of records is  2870

. bysort dtreat: distinct panelID

-------------------------------------------------------------------------------
-> dtreat = 0

         |        Observations
         |      total   distinct
---------+----------------------
 panelID |       1830        183

-------------------------------------------------------------------------------
-> dtreat = 1

         |        Observations
         |      total   distinct
---------+----------------------
 panelID |       1040        104

. bysort dtreat: count 

-------------------------------------------------------------------------------
-> dtreat = 0
  1,830
-------------------------------------------------------------------------------
-> dtreat = 1
  1,040

. 
. 
.    
. *****************************************************************************
> **
. /*                                              TABLE A3                     
>                                         */
. *****************************************************************************
> **
. cd "$rootDir/$dataDir/Final"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Data/Final

. use ACS_panel_balanceTest_recodeVar.dta, clear

. 
. ** Create list of local variables for balance test
. local balance_var pop white foreign male old median_income average_income hom
> e_median non_poor edu_male edu_female prim_prod manu hours

. 
. 
. *------------------------ Balance Test: Table -------------------------*
. eststo clear

. eststo: estpost sum `balance_var' if dtreat == 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
         pop |       637        637   691.6325   3697.073   60.80356    534.223
>     991.439   440569.9 
       white |       637        637   .8352684   .0146813   .1211666   .2995883
>    .9702255   532.0659 
     foreign |       637        637   .0104382   .0000575   .0075834   .0026394
>    .0549219   6.649113 
        male |       637        637   .4942111   .0000692   .0083164   .4560213
>     .518954   314.8125 
         old |       637        637    .134645   .0006014   .0245234   .0702241
>    .1962764   85.76884 
median_inc~e |       637        637   51.54066   111.4094   10.55506       31.6
>      85.052    32831.4 
average_in~e |       635        635   25.74067   21.83809   4.673124     15.714
>      40.857   16345.33 
 home_median |       637        637   768.6358   37947.73   194.8018        461
>        1322     489621 
    non_poor |       637        637   .8523027   .0019426   .0440747   .7010513
>    .9532969   542.9168 
    edu_male |       637        637   .2217622   .0030694   .0554021   .0847944
>    .3944693   141.2625 
  edu_female |       637        637   .2353412   .0028376   .0532693   .0970284
>    .3815072   149.9123 
   prim_prod |       637        637   .0247365   .0005603   .0236711   .0001741
>    .1345447   15.75715 
        manu |       637        637   .1273039   .0026588   .0515635   .0240565
>    .3033075   81.09261 
       hours |       637        637   38.43265   .7110076   .8432127       35.7
>        40.9    24481.6 
(est1 stored)

. eststo: estpost sum `balance_var' if dtreat == 0

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
         pop |      1372       1372   682.4373   3594.671   59.95558    506.036
>    1061.221     936304 
       white |      1372       1372   .6959628   .0385037   .1962235   .1161201
>    .9646632   954.8609 
     foreign |      1372       1372   .0134941   .0001439   .0119943   .0025327
>    .1171315   18.51387 
        male |      1372       1372   .4908959   .0001464   .0120976   .4495972
>    .5449896   673.5091 
         old |      1372       1372   .1237776   .0005625   .0237167   .0529949
>    .1943409   169.8229 
median_inc~e |      1372       1372     54.339   230.8289   15.19306     19.018
>      99.811   74553.11 
average_in~e |      1372       1372   27.73036   72.25349   8.500205     10.786
>       77.09   38046.05 
 home_median |      1372       1372   904.8637   63349.13   251.6925        457
>        1716    1241473 
    non_poor |      1372       1372   .8429041   .0041658    .064543   .5793203
>    .9687206   1156.464 
    edu_male |      1372       1372   .2404924   .0069838   .0835688   .0586258
>    .5557974   329.9556 
  edu_female |      1372       1372   .2506881   .0059429   .0770904   .0704394
>    .5640553   343.9441 
   prim_prod |      1372       1372   .0118471   .0005186   .0227722          0
>    .2557325   16.25426 
        manu |      1372       1372   .1122874   .0023924   .0489124    .023137
>    .2652171   154.0583 
       hours |      1372       1372   38.64665   1.083541   1.040933       35.6
>        44.5    53023.2 
(est2 stored)

. eststo: estpost sum `balance_var'

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max)     e(sum) 
-------------+-----------------------------------------------------------------
-----------------------
         pop |      2009       2009   685.3529   3643.632   60.36251    506.036
>    1061.221    1376874 
       white |      2009       2009   .7401328   .0351434   .1874657   .1161201
>    .9702255   1486.927 
     foreign |      2009       2009   .0125251   .0001185    .010884   .0025327
>    .1171315   25.16298 
        male |      2009       2009    .491947   .0001242    .011145   .4495972
>    .5449896   988.3216 
         old |      2009       2009   .1272234   .0006001   .0244972   .0529949
>    .1962764   255.5917 
median_inc~e |      2009       2009   53.45172   194.5864   13.94942     19.018
>      99.811   107384.5 
average_in~e |      2007       2007   27.10084   57.14027   7.559119     10.786
>       77.09   54391.38 
 home_median |      2009       2009   861.6695   59292.64   243.5008        457
>        1716    1731094 
    non_poor |      2009       2009   .8458841   .0034787   .0589804   .5793203
>    .9687206   1699.381 
    edu_male |      2009       2009   .2345535   .0058165   .0762658   .0586258
>    .5557974   471.2181 
  edu_female |      2009       2009    .245822   .0050074   .0707633   .0704394
>    .5640553   493.8564 
   prim_prod |      2009       2009    .015934   .0005675   .0238229          0
>    .2557325    32.0114 
        manu |      2009       2009   .1170487   .0025245    .050244    .023137
>    .3033075   235.1509 
       hours |      2009       2009    38.5788   .9749287   .9873848       35.6
>        44.5    77504.8 
(est3 stored)

. 
. ** Indent balance variables in table
. foreach v of varlist `balance_var' {
  2.         label variable `v' `"\hspace{0.2cm} `: variable label `v''"'
  3.         }

. 
. ** Output table to LaTex
. cd "$rootDir/$resultDir/Tables"
/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang
> )/JOP/UZ_JOP2021_Replication/Results/Tables

. esttab, main(mean) aux(sd) stats(N, labels("Observations") fmt(0)) ///
>                 label wrap nostar unstack noobs nonumbers nonote width(\hsize
> ) ///
>                 mtitles("With Wind" "Without Wind" "Overall") 

-----------------------------------------------------------
                        With Wind Without Wind      Overall
-----------------------------------------------------------
\hspace{0.2cm} Total        691.6        682.4        685.4
population (thousa~)      (60.80)      (59.96)      (60.36)

\hspace{0.2cm} White        0.835        0.696        0.740
(\%)                      (0.121)      (0.196)      (0.187)

\hspace{0.2cm}             0.0104       0.0135       0.0125
Native, born ..S. ~)    (0.00758)     (0.0120)     (0.0109)

\hspace{0.2cm} Male         0.494        0.491        0.492
(\%)                    (0.00832)     (0.0121)     (0.0111)

\hspace{0.2cm} Older        0.135        0.124        0.127
than 65 (\%)             (0.0245)     (0.0237)     (0.0245)

\hspace{0.2cm}              51.54        54.34        53.45
Median household i~o      (10.56)      (15.19)      (13.95)

\hspace{0.2cm} Per          25.74        27.73        27.10
capita income ~1000)      (4.673)      (8.500)      (7.559)

\hspace{0.2cm}              768.6        904.9        861.7
Median gross rent ~)      (194.8)      (251.7)      (243.5)

\hspace{0.2cm}              0.852        0.843        0.846
Income at or above~l     (0.0441)     (0.0645)     (0.0590)

\hspace{0.2cm} Male         0.222        0.240        0.235
with associate deg~e     (0.0554)     (0.0836)     (0.0763)

\hspace{0.2cm}              0.235        0.251        0.246
Female with associ~n     (0.0533)     (0.0771)     (0.0708)

\hspace{0.2cm}             0.0247       0.0118       0.0159
Employment in agri~)     (0.0237)     (0.0228)     (0.0238)

\hspace{0.2cm}              0.127        0.112        0.117
Employment in manu~)     (0.0516)     (0.0489)     (0.0502)

\hspace{0.2cm} Mean         38.43        38.65        38.58
hours worked              (0.843)      (1.041)      (0.987)
-----------------------------------------------------------
Observations                  637         1372         2009
-----------------------------------------------------------

. 
. esttab using TableA3.tex, booktabs replace ///
>                 refcat(pop "\emph{Demographics}" median_income "\emph{Income 
> \& Poverty}" edu_male "\emph{Education}" prim_prod "\emph{Employment}", nolab
> el) ///
>                 main(mean) aux(sd) stats(N, labels("Observations") fmt(0)) //
> /
>                 label gaps nostar noobs nonumbers nonote width(\hsize) ///
>                 mtitles("With Wind" "Without Wind" "Overall") 
(output written to TableA3.tex)

. 
.         
. *****************************************************************************
> **
. /*                                              TABLES A4-A6                 
>                                 */
. *****************************************************************************
> **
. ** Create interaction and fixed effects
. gen t = year - 2005

. gen inter = t * mean_wp

. 
. egen fixed = group(state year)

. egen district_fixed = group(state district)

. 
. ** Balance regressions
. eststo clear

. local balance_var pop white foreign male old median_income average_income hom
> e_value home_median non_poor hours edu_male edu_female prim_prod manu

. 
. foreach var in `balance_var'{
  2.         eststo: reghdfe `var' inter, absorb(fixed district_fixed) vce(clus
> ter district_fixed) 
  3.         
. }
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       5.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0165
                                                  R-squared       =     0.9577
                                                  Adj R-squared   =     0.9451
                                                  Within R-sq.    =     0.0126
Number of clusters (district_fixed) =        287  Root MSE        =    14.1456

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
         pop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   1.849231    .766433     2.41   0.016     .3406661    3.357796
       _cons |   675.9451   3.899155   173.36   0.000     668.2704    683.6198
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       3.62
Statistics robust to heteroskedasticity           Prob > F        =     0.0583
                                                  R-squared       =     0.9922
                                                  Adj R-squared   =     0.9899
                                                  Within R-sq.    =     0.0055
Number of clusters (district_fixed) =        287  Root MSE        =     0.0188

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
       white |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -.0016206   .0008523    -1.90   0.058    -.0032982     .000057
       _cons |   .7483774   .0043361   172.59   0.000     .7398427    .7569122
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       2.35
Statistics robust to heteroskedasticity           Prob > F        =     0.1264
                                                  R-squared       =     0.9730
                                                  Adj R-squared   =     0.9649
                                                  Within R-sq.    =     0.0037
Number of clusters (district_fixed) =        287  Root MSE        =     0.0020

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
     foreign |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   .0001444   .0000942     1.53   0.126     -.000041    .0003298
       _cons |   .0117905   .0004793    24.60   0.000     .0108472    .0127338
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.57
Statistics robust to heteroskedasticity           Prob > F        =     0.4515
                                                  R-squared       =     0.8936
                                                  Adj R-squared   =     0.8618
                                                  Within R-sq.    =     0.0005
Number of clusters (district_fixed) =        287  Root MSE        =     0.0041

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
        male |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   .0001064   .0001411     0.75   0.452    -.0001714    .0003842
       _cons |   .4914058   .0007179   684.47   0.000     .4899927    .4928189
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       2.44
Statistics robust to heteroskedasticity           Prob > F        =     0.1194
                                                  R-squared       =     0.9767
                                                  Adj R-squared   =     0.9697
                                                  Within R-sq.    =     0.0039
Number of clusters (district_fixed) =        287  Root MSE        =     0.0043

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
         old |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   .0003071   .0001966     1.56   0.119    -.0000798     .000694
       _cons |   .1256611   .0010001   125.65   0.000     .1236927    .1276296
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.12
Statistics robust to heteroskedasticity           Prob > F        =     0.7307
                                                  R-squared       =     0.9918
                                                  Adj R-squared   =     0.9894
                                                  Within R-sq.    =     0.0002
Number of clusters (district_fixed) =        287  Root MSE        =     1.4389

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
median_inc~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   -.020661   .0599594    -0.34   0.731    -.1386787    .0973568
       _cons |   53.55683   .3050379   175.57   0.000     52.95643    54.15724
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      2,007
Absorbing 2 HDFE groups                           F(   1,    286) =       1.02
Statistics robust to heteroskedasticity           Prob > F        =     0.3144
                                                  R-squared       =     0.9925
                                                  Adj R-squared   =     0.9902
                                                  Within R-sq.    =     0.0013
Number of clusters (district_fixed) =        287  Root MSE        =     0.7486

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
average_in~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -.0311974    .030954    -1.01   0.314     -.092124    .0297292
       _cons |   27.25956   .1574797   173.10   0.000     26.94959    27.56952
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       5.17
Statistics robust to heteroskedasticity           Prob > F        =     0.0238
                                                  R-squared       =     0.9930
                                                  Adj R-squared   =     0.9910
                                                  Within R-sq.    =     0.0142
Number of clusters (district_fixed) =        287  Root MSE        =    16.2747

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
  home_value |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -2.258027   .9934633    -2.27   0.024    -4.213454   -.3025994
       _cons |   281.1931    5.05415    55.64   0.000     271.2451    291.1412
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est8 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       5.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0225
                                                  R-squared       =     0.9928
                                                  Adj R-squared   =     0.9906
                                                  Within R-sq.    =     0.0119
Number of clusters (district_fixed) =        287  Root MSE        =    23.5489

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
 home_median |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -2.993878    1.30533    -2.29   0.023     -5.56315   -.4246051
       _cons |   876.9006   6.640743   132.05   0.000     863.8296    889.9715
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est9 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.05
Statistics robust to heteroskedasticity           Prob > F        =     0.8304
                                                  R-squared       =     0.9756
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.0001
Number of clusters (district_fixed) =        287  Root MSE        =     0.0105

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
    non_poor |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -.0001023   .0004771    -0.21   0.830    -.0010413    .0008367
       _cons |   .8464045   .0024271   348.73   0.000     .8416273    .8511817
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est10 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.93
Statistics robust to heteroskedasticity           Prob > F        =     0.3360
                                                  R-squared       =     0.9417
                                                  Adj R-squared   =     0.9242
                                                  Within R-sq.    =     0.0012
Number of clusters (district_fixed) =        287  Root MSE        =     0.2718

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
       hours |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   -.010858   .0112669    -0.96   0.336    -.0330347    .0113186
       _cons |   38.63403   .0573195   674.01   0.000     38.52121    38.74686
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est11 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.66
Statistics robust to heteroskedasticity           Prob > F        =     0.4169
                                                  R-squared       =     0.9917
                                                  Adj R-squared   =     0.9892
                                                  Within R-sq.    =     0.0007
Number of clusters (district_fixed) =        287  Root MSE        =     0.0079

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
    edu_male |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -.0002457   .0003023    -0.81   0.417    -.0008407    .0003492
       _cons |   .2358037   .0015378   153.33   0.000     .2327768    .2388307
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est12 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.68
Statistics robust to heteroskedasticity           Prob > F        =     0.4094
                                                  R-squared       =     0.9907
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.0006
Number of clusters (district_fixed) =        287  Root MSE        =     0.0078

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
  edu_female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |  -.0002157   .0002611    -0.83   0.409    -.0007296    .0002982
       _cons |   .2469193   .0013282   185.91   0.000     .2443051    .2495336
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est13 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       0.52
Statistics robust to heteroskedasticity           Prob > F        =     0.4734
                                                  R-squared       =     0.9862
                                                  Adj R-squared   =     0.9821
                                                  Within R-sq.    =     0.0012
Number of clusters (district_fixed) =        287  Root MSE        =     0.0032

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
   prim_prod |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   -.000127   .0001769    -0.72   0.473    -.0004751    .0002212
       _cons |     .01658   .0008999    18.42   0.000     .0148088    .0183512
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est14 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      2,009
Absorbing 2 HDFE groups                           F(   1,    286) =       1.13
Statistics robust to heteroskedasticity           Prob > F        =     0.2893
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9787
                                                  Within R-sq.    =     0.0017
Number of clusters (district_fixed) =        287  Root MSE        =     0.0073

                       (Std. Err. adjusted for 287 clusters in district_fixed)
------------------------------------------------------------------------------
             |               Robust
        manu |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       inter |   .0003488   .0003286     1.06   0.289    -.0002979    .0009956
       _cons |   .1152741   .0016716    68.96   0.000     .1119839    .1185643
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
          fixed |       175           0         175     |
 district_fixed |       287         287           0    *|
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est15 stored)

. 
. ** Demographics
. esttab est1 est2 est3 est4 est5, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 mtitles("Pop" "White" "Foreign" "Male" ">65")

-------------------------------------------------------------------------------
-------------------------------
                                        (1)             (2)             (3)    
>          (4)             (5)   
                                        Pop           White         Foreign    
>         Male             >65   
-------------------------------------------------------------------------------
-------------------------------
Mean wind potential * time           1.8492**       -0.0016*         0.0001    
>       0.0001          0.0003   
                                   (0.7664)        (0.0009)        (0.0001)    
>     (0.0001)        (0.0002)   
-------------------------------------------------------------------------------
-------------------------------
Observations                           2009            2009            2009    
>         2009            2009   
Districts                               287             287             287    
>          287             287   
-------------------------------------------------------------------------------
-------------------------------
* p<0.10, ** p<0.05, *** p<0.01

. 
. esttab est1 est2 est3 est4 est5 using TableA4.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 mtitles("Pop" "White" "Foreign" "Male" "65+") width(\hsize)
(output written to TableA4.tex)

. 
. ** Income
. esttab est6 est7 est9 est10, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 mtitles("Med Inc" "PC Inc" "Med Rent" "Abv Poverty")

-------------------------------------------------------------------------------
---------------
                                        (1)             (2)             (3)    
>          (4)   
                                    Med Inc          PC Inc        Med Rent    
>  Abv Poverty   
-------------------------------------------------------------------------------
---------------
Mean wind potential * time          -0.0207         -0.0312         -2.9939**  
>      -0.0001   
                                   (0.0600)        (0.0310)        (1.3053)    
>     (0.0005)   
-------------------------------------------------------------------------------
---------------
Observations                           2009            2007            2009    
>         2009   
Districts                               287             287             287    
>          287   
-------------------------------------------------------------------------------
---------------
* p<0.10, ** p<0.05, *** p<0.01

. 
. esttab est6 est7 est9 est10 using TableA5.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 mtitles("Med Inc" "PC Inc" "Med Rent" "Abv Poverty") width(\h
> size)
(output written to TableA5.tex)

. 
. 
. ** Education & Employment
. esttab est11 est12 est13 est14 est15, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 mtitles("Avg Hrs" "Male AS+" "Female AS+" "Empl Ag" "Empl Man
> uf")

-------------------------------------------------------------------------------
-------------------------------
                                        (1)             (2)             (3)    
>          (4)             (5)   
                                    Avg Hrs        Male AS+      Female AS+    
>      Empl Ag      Empl Manuf   
-------------------------------------------------------------------------------
-------------------------------
Mean wind potential * time          -0.0109         -0.0002         -0.0002    
>      -0.0001          0.0003   
                                   (0.0113)        (0.0003)        (0.0003)    
>     (0.0002)        (0.0003)   
-------------------------------------------------------------------------------
-------------------------------
Observations                           2009            2009            2009    
>         2009            2009   
Districts                               287             287             287    
>          287             287   
-------------------------------------------------------------------------------
-------------------------------
* p<0.10, ** p<0.05, *** p<0.01

. 
. esttab est11 est12 est13 est14 est15 using TableA6.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") 
> fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) //
> /
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.
> 10 ** 0.05 *** 0.01) ///
>                 mtitles("Avg Hrs" "Male AS+" "Female AS+" "Empl Ag" "Empl Man
> uf") width(\hsize) 
(output written to TableA6.tex)

. 
.                 
.                 
. ** Close log file
. log close
      name:  <unnamed>
       log:  /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urp
> elainen Zhang)/JOP/UZ_JOP2021_Replication/Analysis/logSTATA/003_balanceTest.s
> mcl
  log type:  smcl
 closed on:   6 Nov 2021, 19:49:47
-------------------------------------------------------------------------------
